Big Data Guide to Commercializing Automotive Data-Driven Services

Big Data Guide to Commercializing Automotive Data-Driven Services

Three Pillars for Successfully Commercializing Data Use

Given the market challenges so far in terms of establishing successful revenue streams to offset the expenses of providing connected car technology, OEMs are still hunting for a business model that will work—for customers, partners, and the OEMs themselves. Trends in the automotive industry point toward always-on data capture and distribution over the lifespan of a vehicle. Tesla has successfully integrated this approach into their brand, strengthening their tie to customers by focusing on personalized experiences. Over years, the company accumulates a wealth of data about each vehicle’s operational history—from both a vehicle and driver behavior perspective.

The value of the services and intelligence that can be derived from this data is the key. Data plus analytics yields intelligence and that intelligence has tremendous value across many markets. The considerable challenges of profiting from connected car subscription services has troubled the industry since these services were first introduced. OEMs, however, can monetize the data collected in a variety of innovative ways—as long as they manage certain essential considerations well. The value of deep insights about the drivers of their vehicles is a bonus beyond the basic monetization of the data.

The data commercialization challenges faced by automakers typically involve three major pillars, all of which must be resolved as a part of an effective business model for providing connected car services.

Pillar 1: Data Management: Integrity, Security, and Privacy

Building a business model that involves the capture, validation, provisioning, and distribution of massive volumes of vehicle and driver information requires considerable expertise and a supporting infrastructure. Under the umbrella of data management, several specific areas of data handling must be addressed to meet the concerns of OEMs.

TransparencyEvery byte of data about a vehicle and the driver operating that vehicle must be subject to full transparency and consent. The driver must be consistently informed of what data is being collected, how it will be used, how long it will be stored, who else will have access to it, and what prerogative does the driver or vehicle owner have for terminating consent given for the use of this data. The most effective Big Data applications associated with automotive useassume an always-on model that can capture vehicle data throughout its cradle-to-grave lifecycle, whether it is in motion or parked, and includes a very wide range of parameters. The vehicle operator must give consent for the collection and use of every one of these parameters—from vehicle location to data capture of driver operations (speed, braking, the G-force of turns, and so on) to identifying the person behind the wheel at any given moment.

PrivacyDifferent jurisdictions around the world have varying levels of regulations and mandates in terms of an individual’s data privacy. These laws and mandates can be complex and in some case overlap depending on the region and even the state. Data privacy is important both for legal and regulatory measures with which the OEM and any partners must comply, in addition to the basic business principle of respecting and protecting all information that relates to their customers.Failure to pay attention to data privacy could subject the OEM to fines or loss of their stature in the industry, as well as casting a dark shadow over the way that they are perceived by customers. When working through a service or intermediary that is handling data on behalf of an OEM, the OEM must ensure that strong data privacy protections are included in any agreement and followed consistently.

SecurityEnsuring secure data exchanges is integral to any data commercialization effort. Part of this involves monitoring and tracking where data is being sent, where it originates, and whether encryption is used consistently to secure data while in transit. Any areas in the data path that potentially allow intrusions or are vectors for abuse should be remediated to mitigate the risk. Risk has many different dimensions and security should always be implemented anywhere that a risk potential is identified. With rigorous, secure data protection mechanisms in place, hacking becomes a non-issue, rather than a concern for OEMs.

Required Vehicle EquipmentMany different mechanisms exist for capturing and transmitting vehicle and driver data. The hardware and software supporting this effort should be factored into the plan for commercializing data. The range of hardware and vehicle types supported introduces a layer of complexity. Of critical importance to any commercialization program is the capability of collecting, cleansing, normalizing, and unifying collected data so that irrespective of the OEM’s hardware decision, the vehicle and driving information is delivered to each beneficiary in a uniform, usable format. Commercialization demands that the management of data across this spectrum of devices in a consistent, verifiable manner. To maximize the data value, this management should include the earliest devices in the market, all those operating today, and new devices as they are introduced (always maintaining backward compatibility).

Groupement ADAS is a Team of innovative companies with over 20 years experience in the field of technologies used in assistance driver systems (design, implementation and integration of ADAS in vehicles for safety features, driver assistance, partial delegation to the autonomous vehicle).